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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing II"

共 2638 条记录，以下是211-220 订阅

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SENSOR image SIMULATION USING neural networks 4

SENSOR IMAGE SIMULATION USING NEURAL NETWORKS

引用

4th International conference on applications of artificial neural networks

作者： BAUMANN, EW ZVOLANEK, B MCDONNELL DOUGLAS CORP MCDONNELL DOUGLAS TRAINING SYSTST LOUISMO 63166

ISBN: (纸本)0819412015

Simulation of infrared, radar, and other imaging sensors plays an important role in the planning and rehearsal of military missions and in the training of mission personnel. The challenge is to develop technology that will support the rapid use of reconnaissance imagery to generate cockpit sensor displays that accurately represent the real world while insuring correlation among the out-the-window scenes and sensor displays. This paper describes a novel, neural-network-based technique for infrared and radar image simulation directly from multi-spectral imagery (MSI). Source imagery, its processing using neural networks, and infrared and radar image simulation results are presented. Issues related to MSI database generation are also described.

关键词： image sensors Sensors Databases Multispectral imaging neural networks Radar Infrared imaging Infrared radiation Synthetic aperture radar Data modeling

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Spline-based neural networks for digital image interpolation

Spline-based neural networks for digital image interpolation

引用

conference on applications of artificial neural networks in image processing VI

作者： Gustafson, SC Meyer, GJ USAF Inst Technol ENG Wright Patterson AFB OH 45433 USA

ISBN: (纸本)0819439835

Adaptive spline interpolation (which is equivalent to the use of a type of radial basis function neural network) is investigated for digital image interpolation (i.e., for resolution enhancement). Test image results indicate that adaptive spline interpolation of a low-resolution image is superior to non-adaptive interpolation if the adjustable parameters are chosen to yield the best match to a known object in a corresponding high-resolution image.

关键词： image enhancement image interpolation neural networks image resolution image sensors Resolution enhancement technologies Sensors Algorithm development Linear filtering Sinc filters

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Digital Information processing and Communications, Part ii 2011

引用

丛书名： Communications in Computer and Information Science

2011年

作者： Vaclav Snasael Jan Platos Eyas El-Qawasmeh

ISBN: (数字)9783642224102

ISBN: (纸本)9783642224096

This two-volume-set (CCIS 188 and CCIS 189) constitutes the refereed proceedings of the International conference on Digital Information processing and Communications, ICDIPC 2011, held in Ostrava, Czech Republic, in July 2011. The 91 revised full papers of both volumes presented together with 4 invited talks were carefully reviewed and selected from 235 submissions. The papers are organized in topical sections on network security; Web applications; data mining; neural networks; distributed and parallel processing; biometrics technologies; e-learning; information ethics; image processing; information and data management; software engineering; data compression; networks; computer security; hardware and systems; multimedia; ad hoc network; artificial intelligence; signal processing; cloud computing; forensics; security; software and systems; mobile networking; and some miscellaneous topics in digital information and communications.

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Wavelet-cellular neural network architecture and learning algorithm

Wavelet-cellular neural network architecture and learning al...

引用

conference on Independent Component Analyses, Wavelets, Unsupervised Smart Sensors and neural networks ii

作者： Bal, A Ucan, ON Pastaci, H Alam, M Univ S Alabama Dept Elect & Comp Engn Mobile AL 36688 USA

ISBN: (纸本)0819453625

Cellular neural networks (CNN) provides fast parallel computational capability for image processing applications. The behavior of the CNN is defined by two template matrices. In this paper, adjustment of these template-matrix coefficients have been realized using supervised learning algorithm based on back-propagation technique and wavelet function. Back-propagation algorithm has been modified for dynamic behavior of CNN. Wavelet function is utilized to provide the activation function derivation in this learning algorithm. The supervised learning algorithm is then executed to obtain a compact CNN architecture, called as Wave-CNN. The proposed new learning algorithm and Wave-CNN architecture performance have been tested for 2D image processing applications.

关键词： neural networks Evolutionary algorithms image processing Machine learning Wavelets Algorithm development Matrices

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Learning a Wavelet-Like Auto-Encoder to Accelerate Deep neural networks 32

Learning a Wavelet-Like Auto-Encoder to Accelerate Deep Neur...

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32nd AAAI conference on artificial Intelligence / 30th Innovative applications of artificial Intelligence conference / 8th AAAI Symposium on Educational Advances in artificial Intelligence

作者： Chen, Tianshui Lin, Liang Zuo, Wangmeng Luo, Xiaonan Zhang, Lei Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Guangdong Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Harbin Heilongjiang Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

ISBN: (纸本)9781577358008

Accelerating deep neural networks (DNNs) has been attracting increasing attention as it can benefit a wide range of applications, e.g., enabling mobile systems with limited computing resources to own powerful visual recognition ability. A practical strategy to this goal usually relies on a two-stage process: operating on the trained DNNs (e.g., approximating the convolutional filters with tensor decomposition) and fine-tuning the amended network, leading to difficulty in balancing the trade-off between acceleration and maintaining recognition performance. In this work, aiming at a general and comprehensive way for neural network acceleration, we develop a Wavelet-like Auto-Encoder (WAE) that decomposes the original input image into two low-resolution channels (sub-images) and incorporate the WAE into the classification neural networks for joint training. The two decomposed channels, in particular, are encoded to carry the low-frequency information (e.g., image profiles) and high-frequency (e.g., image details or noises), respectively, and enable reconstructing the original input image through the decoding process. Then, we feed the low-frequency channel into a standard classification network such as VGG or ResNet and employ a very lightweight network to fuse with the high-frequency channel to obtain the classification result. Compared to existing DNN acceleration solutions, our framework has the following advantages: i) it is tolerant to any existing convolutional neural networks for classification without amending their structures;ii) the WAE provides an interpretable way to preserve the main components of the input image for classification.

关键词： Deep neural networks

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AUTONOMOUS PARTS ASSEMBLY - COMPARISON OF ART AND NEOCOGNITRON 4

AUTONOMOUS PARTS ASSEMBLY - COMPARISON OF ART AND NEOCOGNITR...

引用

4th International conference on applications of artificial neural networks

作者： ROSANDICH, RG OZBAYOGLU, MA RODDIGER, EW DAGLI, CH UNIV MISSOURI DEPT ENGN MANAGEMENTROLLAMO 65401

ISBN: (纸本)0819412015

In this paper, we present the performance analysis of three different neural network paradigms, ART-1, ARTMAP inspired ART-1 and Neocognitron, for part recognition in an autonomous assembly system. This intelligent manufacturing system integrates machine vision, neural networks and robotics in order to identify, locate and assemble randomly places components on printed circuit boards requiring precision assembly. The system uses an IBM 7547 robot controlled by an IBM PS/2 computer, a CCD camera and an image capture card. The electronic components are identified and located by using artificial neural networks. The system's component location and identification accuracy are tested on all test components. The results show that the neocognitron-based system performed better than the other two systems.

关键词： neural networks Cameras artificial neural networks Relays Manufacturing Capacitors Neurons Intelligence systems Pattern recognition image processing

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Vision system for automatic recognition of selected road users 25

Vision system for automatic recognition of selected road use...

引用

25th IEEE conference on Signal processing - Algorithms, Architectures, Arrangements, and applications (IEEE SPA)

作者： Balcerek, Julian Konieczka, Adam Pawlowski, Pawel Rusinek, Wojciech Trojanowski, Wojciech Poznan Univ Tech Div Signal Proc & Elect Syst Inst Automat Control & Robot Poznan Poland

ISBN: (数字)9788362065424

ISBN: (纸本)9788362065424

In this article, a system for automatic recognition of selected road users using artificial neural networks is proposed. Five models of neural networks were tested with various configurations. With the special prepared image database, training, validation and tests were conducted to identify five classes of road users: cyclists, people on electric scooters, roller skates, pedestrians and people using personal transport devices. A web application that recognize these classes of road users was also prepared.

关键词： recognition of road traffic participants vision system artificial neural networks mobile application image recognition image processing

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A Software Tool for Complex-Valued neural Network: CV-ANN 23

A Software Tool for Complex-Valued Neural Network: CV-ANN

引用

23nd Signal processing and Communications applications conference (SIU)

作者： Guruler, Huseyin Peker, Musa Mugla Sitki Kocman Univ Bilisim Sistemleri Muhendisligi Bolumu Mugla Turkey Samandira Mesleki & Tekn Anadolu Lisesi Bilisim Teknol Bolumu Istanbul Turkey

ISBN: (纸本)9781467373869

Complex valued artificial neural networks (CVANN);is a kind of artificial neural network of which parameters such as weight, threshold, input and output consisting of complex numbers. In recent years, CVANN has increasing interest because CVANN gives better results in nonlinear signal processing and image processing problems. There has been no software introduced in the literature for CVANN yet. In this study, CVANN software (CV-ANN), which is developed with object-oriented programming language C#, is presented. In this software tool which is designed with a user-friendly interface, user can set the parameter values of CVANN. Changes in neural network output with respect to entered parameter values are showed graphically. Developed software can facilitate the work of researchers in academic and scientific studies. At the same time, this software can be useful as an educational tool in the sense of making the working mechanism of CVANN clearer. CV-ANN is tested on XOR problem which is a standard test and CV-ANN proves its reliability performing the classification with the accuracy obtained by the same algorithm in the literature.

关键词： Nonlinear signal processing Complex valued network artificial neural networks Software tool

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Segmentation of remote-sensing images by artificial neural networks

Segmentation of remote-sensing images by artificial neural n...

引用

IEEE 12th Signal processing and Communications applications conference

作者： Ölmez, T Dokur, Z Istanbul Tech Univ Elekt & Elektron Muhendisligi Bolumu Maslak Turkey

ISBN: (纸本)0780383184

In this study, a novel unsupervised incremental neural network is proposed for the segmentation of remote sensing images. Feature vectors are formed by the intensity of one pixel of each channel. The trainning set of DAYS network is formed by using all pixels of the image. The remote sensing image is segmented according to the decision of the network. In the study, the segmentation results of DAYS and Kohonen networks are compared

关键词： image segmentation

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Optimization of fuzzy-neural structure through genetic algorithms and its application in artificial odor recognition system

Optimization of fuzzy-neural structure through genetic algor...

引用

Asia-Pacific conference on Circuits and Systems

作者： Kusumoputro, B Irwanto, P Jatmiko, W Univ Indonesia Fac Comp Sci Jakarta 10002 Indonesia

ISBN: (纸本)0780376900

Fuzzy neural networks is gaining much researcher's interest and have attracted considerably attention recently, due to its diverse applications in such fields as pattern recognition, image processing and control. However, this type of neural system, as same with that of multilayer perceptron, has a drawback due to its huge neural connections. In this article we proposed a method for optimizing the structure of a fuzzy artificial neural networks (FANN) through genetic algorithms. This genetic algorithm (GA) is used to optimize the number of weight connections in a neural network structure, by evolutionary calculating the fitness function of those structures as individuals in a population. The developed optimized fuzzy neural is then applied as the pattern recognition in our odor recognition system. Experimental results show that the optimized neural system provides higher recognition capability compare with that of unoptimized neural system. Recognition rate of the unoptimized neural structure is 70.4% and could be increased up to 85.2% in the optimized neural system. It is also shown that the computational cost of the optimized structure of neural system is also lower than the unoptimized structure.

关键词： artificial neural networks Fuzzy control Fuzzy neural networks Fuzzy systems Genetic algorithms image processing Multilayer perceptrons Optimization methods Pattern recognition Process control

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